In recent years, concepts and tools from dynamical systems theory have been successfully applied to the study of movement systems, contradicting traditional views of variability as noise or error. From this perspective, it is apparent that variability in movement systems is omnipresent and unavoidable due to the distinct constraints that shape each individual's behaviour. In this position paper, it is argued that trial-to-trial movement variations within individuals and performance differences observed between individuals may be best interpreted as attempts to exploit the variability that is inherent within and between biological systems. That is, variability in movement systems helps individuals adapt to the unique constraints (personal, task and environmental) impinging on them across different timescales. We examine the implications of these ideas for sports medicine, by: (i) focusing on intra-individual variability in postural control to exemplify within-individual real-time adaptations to changing informational constraints in the performance environment; and (ii) interpreting recent evidence on the role of the angiotensin-converting enzyme gene as a genetic (developmental) constraint on individual differences in physical performance. The implementation of a dynamical systems theoretical interpretation of variability in movement systems signals a need to re-evaluate the ubiquitous influence of the traditional 'medical model' in interpreting motor behaviour and performance constrained by disease or injury to the movement system. Accordingly, there is a need to develop new tools for providing individualised plots of motor behaviour and performance as a function of key constraints. Coordination profiling is proposed as one such alternative approach for interpreting the variability and stability demonstrated by individuals as they attempt to construct functional, goal-directed patterns of motor behaviour during each unique performance. Finally, the relative contribution of genes and training to between-individual performance variation is highlighted, with the conclusion that dynamical systems theory provides an appropriate multidisciplinary theoretical framework to explain their interaction in supporting physical performance.
This article discusses the main substantive issues surrounding performance analysis and considers future directions in this recently formed sub-discipline of sport science. It is argued that it is insufficient to bring together sport biomechanics and notational analysis on the basis that they share a number of commonalities, such as they both aim to enhance performance, they both make extensive use of information and communications technology, and both are concerned with producing valid and reliable data. Rather, it is suggested that the common factor linking sport biomechanics and notational analysis is that they can both be used to measure and describe the same phenomenon (i.e. emergent pattern formation) at different scales of analysis (e.g. intra-limb, inter-limb and torso, and inter-personal). Key concepts from dynamical system theory, such as self-organization and constraints, can then be used to explain stability, variability and transitions among coordinative states. By adopting a constraints-based approach, performance analysis could be effectively opened up to sport scientists from other sub-disciplines of sport science, such as sport physiology and psychology, rather than solely being the preserve of sport biomechanists and notational analysts. To conclude, consideration is given to how a more unified approach, based on the tenets of dynamical systems theory, could impact on the future of performance analysis.
In sport and exercise biomechanics, forward dynamics analyses or simulations have frequently been used in attempts to establish optimal techniques for performance of a wide range of motor activities. However, the accuracy and validity of these simulations is largely dependent on the complexity of the mathematical model used to represent the neuromusculoskeletal system. It could be argued that complex mathematical models are superior to simple mathematical models as they enable basic mechanical insights to be made and individual-specific optimal movement solutions to be identified. Contrary to some claims in the literature, however, we suggest that it is currently not possible to identify the complete optimal solution for a given motor activity. For a complete optimization of human motion, dynamical systems theory implies that mathematical models must incorporate a much wider range of organismic, environmental and task constraints. These ideas encapsulate why sports medicine specialists need to adopt more individualized clinical assessment procedures in interpreting why performers' movement patterns may differ.
The main aim of this study was to identify significant relationships between selected anthropometric and kinematic variables and ball release speed. Nine collegiate fast-medium bowlers (mean +/- s: age 21.0 +/- 0.9 years, body mass 77.2 +/- 8.1 kg, height 1.83 +/- 0.1 m) were filmed and reconstructed three-dimensionally. Ball release speeds were measured by a previously validated Speedchek Personal Sports Radar (Tribar Industries, Canada). Relationships between selected anthropometric variables and ball release speed and between kinematic variables and ball release speed were investigated using Pearson's product-moment correlation coefficients (r). A significant relationship was found between the horizontal velocity during the pre-delivery stride (r = 0.728, P < 0.05) and ball release speed (31.5 +/- 1.9 m(-1) s(-1)). We believe that the high correlation was due to the bowlers using techniques that allowed them to contribute more of the horizontal velocity created during the run-up to ball release speed. We also found that the angular velocity (40.6 +/- 3.4 rad x s(-1)) of the right humerus had a low correlation (r = 0.358, P > 0.05) with ball release speed. Although the action of the wrist was not analysed because of an inadequate frame rate, we found high correlations between ball release speed and shoulder-wrist length (661 +/- 31 mm; r = 0.626, P < 0.05) and ball release speed and total arm length (860 +/- 36 mm; r = 0.583, P < 0.05). We conclude that the variance in release speed within this group may be accounted for by the difference in radial length between the axis of rotation at the glenohumeral joint and the release point.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.